Enterprise resource planning (ERP) systems allow for the integration of internal and external management information across an entire organization, including financial/accounting, manufacturing, sales and service, customer relationship management, and the like. The purpose of ERP is to facilitate the flow of information between business functions inside the organization and management connections to outside entitles. Data with ERP, however, may not always be valid. For example, for an employee record, there may be a number of fields, including social security number, address, and postal code. Through profiling, it may be discovered that some of these fields have bad information, or at least are suspected to have bad information due to the patterns of data in all employee records. In one example, the country listed for addresses for some employees may be suspected as bad data if the values for the country field are outliers. If, for example, 30% of the employee records list USA as the country, 30% list CAN (for Canada), and 33% list JAP (for Japan), then if less than 1% list “USS” and “XX”, then those records listing USS or XX may be viewed as potentially bad data, either through typographical errors during input (e.g., the user meant to type USA instead USS), or through intentionally leaving a placeholder (e.g., user put “XX” because the country was unknown). In such cases, it is beneficial to clean up this bad data and prevent future records from having such bad values entered on them. Validation rules can be used to do this, but currently validation rules require a lot of manual effort.